{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bb56c35b",
   "metadata": {},
   "source": [
    "# 股票预测 - 每个股票单独训练LightGBM模型"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efa657ed",
   "metadata": {},
   "source": [
    "## 导库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6dc64c05",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import lightgbm as lgb\n",
    "import joblib\n",
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams[\"font.sans-serif\"] = [\"SimHei\"]\n",
    "plt.rcParams[\"axes.unicode_minus\"] = False\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30f9e7e7",
   "metadata": {},
   "source": [
    "## 参数配置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2579540f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据目录: ./../../data\n",
      "输出目录: ./../../output\n",
      "模型目录: ./../../model\n",
      "序列长度: 32\n",
      "LightGBM参数: {'objective': 'regression', 'metric': 'mse', 'boosting_type': 'gbdt', 'num_leaves': 31, 'learning_rate': 0.01, 'feature_fraction': 0.9, 'bagging_fraction': 0.8, 'bagging_freq': 5, 'verbose': -1, 'random_state': 42, 'n_estimators': 200}\n"
     ]
    }
   ],
   "source": [
    "DATA_DIR = \"./../../data\"\n",
    "OUTPUT_DIR = \"./../../output\"\n",
    "MODEL_DIR = \"./../../model\"\n",
    "\n",
    "# LightGBM模型参数配置\n",
    "seq_len = 32  # 序列长度，用于构造时间窗口特征\n",
    "lgb_params = {\n",
    "    \"objective\": \"regression\",\n",
    "    \"metric\": \"mse\",\n",
    "    \"boosting_type\": \"gbdt\",\n",
    "    \"num_leaves\": 31,\n",
    "    \"learning_rate\": 0.01,\n",
    "    \"feature_fraction\": 0.9,\n",
    "    \"bagging_fraction\": 0.8,\n",
    "    \"bagging_freq\": 5,\n",
    "    \"verbose\": -1,\n",
    "    \"random_state\": 42,\n",
    "    \"n_estimators\": 200,\n",
    "}\n",
    "# 验证集天数设置\n",
    "validation_days = 5\n",
    "\n",
    "print(f\"数据目录: {DATA_DIR}\")\n",
    "print(f\"输出目录: {OUTPUT_DIR}\")\n",
    "print(f\"模型目录: {MODEL_DIR}\")\n",
    "print(f\"序列长度: {seq_len}\")\n",
    "print(f\"LightGBM参数: {lgb_params}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "942242cc",
   "metadata": {},
   "source": [
    "## 数据加载"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ce2d3c33",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据形状: (635729, 12)\n",
      "股票数量: 300\n"
     ]
    },
    {
     "data": {
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       "columns": [
        {
         "name": "index",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "StockCode",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "Date",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "Open",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "Close",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "High",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "Low",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "Volume",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "Turnover",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "Amplitude",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PriceChange",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "TurnoverRate",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PriceChangePercentage",
         "rawType": "float64",
         "type": "float"
        }
       ],
       "ref": "d12798ea-5a2b-4c6a-aeb0-155f58e71ae5",
       "rows": [
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         "0",
         "600000",
         "2015-04-20",
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         "8.42",
         "-0.49",
         "3.84",
         "-5.22"
        ],
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         "2015-04-21",
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         "3681947",
         "6615540736.0",
         "3.49",
         "0.18",
         "2.47",
         "2.02"
        ],
        [
         "2",
         "600000",
         "2015-04-22",
         "9.17",
         "9.31",
         "9.35",
         "9.02",
         "4207667",
         "7712130816.0",
         "3.64",
         "0.24",
         "2.82",
         "2.65"
        ]
       ],
       "shape": {
        "columns": 12,
        "rows": 3
       }
      },
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       "<div>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Turnover</th>\n",
       "      <th>Amplitude</th>\n",
       "      <th>PriceChange</th>\n",
       "      <th>TurnoverRate</th>\n",
       "      <th>PriceChangePercentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>600000</td>\n",
       "      <td>2015-04-20</td>\n",
       "      <td>9.47</td>\n",
       "      <td>8.89</td>\n",
       "      <td>9.47</td>\n",
       "      <td>8.68</td>\n",
       "      <td>5724358</td>\n",
       "      <td>1.044673e+10</td>\n",
       "      <td>8.42</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>3.84</td>\n",
       "      <td>-5.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>600000</td>\n",
       "      <td>2015-04-21</td>\n",
       "      <td>8.79</td>\n",
       "      <td>9.07</td>\n",
       "      <td>9.10</td>\n",
       "      <td>8.79</td>\n",
       "      <td>3681947</td>\n",
       "      <td>6.615541e+09</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.18</td>\n",
       "      <td>2.47</td>\n",
       "      <td>2.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>600000</td>\n",
       "      <td>2015-04-22</td>\n",
       "      <td>9.17</td>\n",
       "      <td>9.31</td>\n",
       "      <td>9.35</td>\n",
       "      <td>9.02</td>\n",
       "      <td>4207667</td>\n",
       "      <td>7.712131e+09</td>\n",
       "      <td>3.64</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.82</td>\n",
       "      <td>2.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   StockCode        Date  Open  Close  High   Low   Volume      Turnover  \\\n",
       "0     600000  2015-04-20  9.47   8.89  9.47  8.68  5724358  1.044673e+10   \n",
       "1     600000  2015-04-21  8.79   9.07  9.10  8.79  3681947  6.615541e+09   \n",
       "2     600000  2015-04-22  9.17   9.31  9.35  9.02  4207667  7.712131e+09   \n",
       "\n",
       "   Amplitude  PriceChange  TurnoverRate  PriceChangePercentage  \n",
       "0       8.42        -0.49          3.84                  -5.22  \n",
       "1       3.49         0.18          2.47                   2.02  \n",
       "2       3.64         0.24          2.82                   2.65  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(os.path.join(DATA_DIR, \"train.csv\"))\n",
    "\n",
    "# 列名映射\n",
    "column_mapping = {\n",
    "    \"股票代码\": \"StockCode\",\n",
    "    \"日期\": \"Date\",\n",
    "    \"开盘\": \"Open\",\n",
    "    \"收盘\": \"Close\",\n",
    "    \"最高\": \"High\",\n",
    "    \"最低\": \"Low\",\n",
    "    \"成交量\": \"Volume\",\n",
    "    \"成交额\": \"Turnover\",\n",
    "    \"振幅\": \"Amplitude\",\n",
    "    \"涨跌额\": \"PriceChange\",\n",
    "    \"换手率\": \"TurnoverRate\",\n",
    "    \"涨跌幅\": \"PriceChangePercentage\",\n",
    "}\n",
    "\n",
    "df.rename(columns=column_mapping, inplace=True)\n",
    "print(f\"数据形状: {df.shape}\")\n",
    "print(f\"股票数量: {df['StockCode'].nunique()}\")\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bb50ee7",
   "metadata": {},
   "source": [
    "## 特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "11e53b42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "添加技术指标后的特征数量: 25\n",
      "新增技术指标列: ['MA.MA1', 'MA.MA2', 'MA.MA3', 'MA.MA4', 'MA.MA5', 'MA.MA6', 'KDJ.K', 'KDJ.D', 'KDJ.J', 'MACD.DIFF', 'MACD.DEA', 'MACD.MACD', 'CCI.CCI']\n"
     ]
    },
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         "name": "MACD.MACD",
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         "name": "CCI.CCI",
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       "      <th>StockCode</th>\n",
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       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>High</th>\n",
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       "      <td>600000</td>\n",
       "      <td>2015-04-21</td>\n",
       "      <td>8.79</td>\n",
       "      <td>9.07</td>\n",
       "      <td>9.10</td>\n",
       "      <td>8.79</td>\n",
       "      <td>3681947</td>\n",
       "      <td>6.615541e+09</td>\n",
       "      <td>3.49</td>\n",
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       "      <td>8.665667</td>\n",
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       "      <td>8.147583</td>\n",
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       "      <td>0.003879</td>\n",
       "      <td>0.031030</td>\n",
       "      <td>-66.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>600000</td>\n",
       "      <td>2015-04-22</td>\n",
       "      <td>9.17</td>\n",
       "      <td>9.31</td>\n",
       "      <td>9.35</td>\n",
       "      <td>9.02</td>\n",
       "      <td>4207667</td>\n",
       "      <td>7.712131e+09</td>\n",
       "      <td>3.64</td>\n",
       "      <td>0.24</td>\n",
       "      <td>...</td>\n",
       "      <td>8.665667</td>\n",
       "      <td>8.662</td>\n",
       "      <td>8.147583</td>\n",
       "      <td>49.367089</td>\n",
       "      <td>35.864979</td>\n",
       "      <td>76.371308</td>\n",
       "      <td>0.059684</td>\n",
       "      <td>0.015040</td>\n",
       "      <td>0.089288</td>\n",
       "      <td>100.000000</td>\n",
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       "<p>3 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   StockCode        Date  Open  Close  High   Low   Volume      Turnover  \\\n",
       "0     600000  2015-04-20  9.47   8.89  9.47  8.68  5724358  1.044673e+10   \n",
       "1     600000  2015-04-21  8.79   9.07  9.10  8.79  3681947  6.615541e+09   \n",
       "2     600000  2015-04-22  9.17   9.31  9.35  9.02  4207667  7.712131e+09   \n",
       "\n",
       "   Amplitude  PriceChange  ...    MA.MA4  MA.MA5    MA.MA6      KDJ.K  \\\n",
       "0       8.42        -0.49  ...  8.665667   8.662  8.147583  26.582278   \n",
       "1       3.49         0.18  ...  8.665667   8.662  8.147583  34.177215   \n",
       "2       3.64         0.24  ...  8.665667   8.662  8.147583  49.367089   \n",
       "\n",
       "       KDJ.D      KDJ.J  MACD.DIFF  MACD.DEA  MACD.MACD     CCI.CCI  \n",
       "0  26.582278  26.582278   0.000000  0.000000   0.000000  -66.666667  \n",
       "1  29.113924  44.303797   0.019394  0.003879   0.031030  -66.666667  \n",
       "2  35.864979  76.371308   0.059684  0.015040   0.089288  100.000000  \n",
       "\n",
       "[3 rows x 25 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 添加技术指标特征\n",
    "# 计算移动平均线 (MA)\n",
    "df[\"MA.MA1\"] = df.groupby(\"StockCode\")[\"Close\"].transform(\n",
    "    lambda x: x.rolling(window=5).mean()\n",
    ")\n",
    "df[\"MA.MA2\"] = df.groupby(\"StockCode\")[\"Close\"].transform(\n",
    "    lambda x: x.rolling(window=10).mean()\n",
    ")\n",
    "df[\"MA.MA3\"] = df.groupby(\"StockCode\")[\"Close\"].transform(\n",
    "    lambda x: x.rolling(window=20).mean()\n",
    ")\n",
    "df[\"MA.MA4\"] = df.groupby(\"StockCode\")[\"Close\"].transform(\n",
    "    lambda x: x.rolling(window=30).mean()\n",
    ")\n",
    "df[\"MA.MA5\"] = df.groupby(\"StockCode\")[\"Close\"].transform(\n",
    "    lambda x: x.rolling(window=60).mean()\n",
    ")\n",
    "df[\"MA.MA6\"] = df.groupby(\"StockCode\")[\"Close\"].transform(\n",
    "    lambda x: x.rolling(window=120).mean()\n",
    ")\n",
    "\n",
    "\n",
    "# 计算KDJ指标\n",
    "def calculate_kdj(data, n=10, m1=3, m2=3):\n",
    "    data = data.copy()\n",
    "    low_list = data[\"Low\"].rolling(window=n, min_periods=1).min()\n",
    "    high_list = data[\"High\"].rolling(window=n, min_periods=1).max()\n",
    "\n",
    "    rsv = (data[\"Close\"] - low_list) / (high_list - low_list) * 100\n",
    "    data[\"KDJ.K\"] = rsv.ewm(alpha=1 / m1, adjust=False).mean()\n",
    "    data[\"KDJ.D\"] = data[\"KDJ.K\"].ewm(alpha=1 / m2, adjust=False).mean()\n",
    "    data[\"KDJ.J\"] = 3 * data[\"KDJ.K\"] - 2 * data[\"KDJ.D\"]\n",
    "    return data\n",
    "\n",
    "\n",
    "# 按股票代码分组计算KDJ\n",
    "for stock_code, group in df.groupby(\"StockCode\"):\n",
    "    kdj_data = calculate_kdj(group)\n",
    "    df.loc[kdj_data.index, [\"KDJ.K\", \"KDJ.D\", \"KDJ.J\"]] = kdj_data[\n",
    "        [\"KDJ.K\", \"KDJ.D\", \"KDJ.J\"]\n",
    "    ]\n",
    "\n",
    "\n",
    "# 计算MACD指标\n",
    "def calculate_macd(data, short_window=10, long_window=26, signal_window=9):\n",
    "    data = data.copy()\n",
    "    data[\"MACD.DIFF\"] = (\n",
    "        data[\"Close\"].ewm(span=short_window, adjust=False).mean()\n",
    "        - data[\"Close\"].ewm(span=long_window, adjust=False).mean()\n",
    "    )\n",
    "    data[\"MACD.DEA\"] = data[\"MACD.DIFF\"].ewm(span=signal_window, adjust=False).mean()\n",
    "    data[\"MACD.MACD\"] = 2 * (data[\"MACD.DIFF\"] - data[\"MACD.DEA\"])\n",
    "    return data\n",
    "\n",
    "\n",
    "# 按股票代码分组计算MACD\n",
    "for stock_code, group in df.groupby(\"StockCode\"):\n",
    "    macd_data = calculate_macd(group)\n",
    "    df.loc[macd_data.index, [\"MACD.DIFF\", \"MACD.DEA\", \"MACD.MACD\"]] = macd_data[\n",
    "        [\"MACD.DIFF\", \"MACD.DEA\", \"MACD.MACD\"]\n",
    "    ]\n",
    "\n",
    "\n",
    "# 计算CCI指标\n",
    "def calculate_cci(data, n=10):\n",
    "    data = data.copy()\n",
    "    tp = (data[\"High\"] + data[\"Low\"] + data[\"Close\"]) / 3\n",
    "    ma = tp.rolling(window=n, min_periods=1).mean()\n",
    "    md = tp.rolling(window=n, min_periods=1).apply(lambda x: abs(x - x.mean()).mean())\n",
    "    data[\"CCI.CCI\"] = (tp - ma) / (0.015 * md)\n",
    "    return data\n",
    "\n",
    "\n",
    "# 按股票代码分组计算CCI\n",
    "for stock_code, group in df.groupby(\"StockCode\"):\n",
    "    cci_data = calculate_cci(group)\n",
    "    df.loc[cci_data.index, [\"CCI.CCI\"]] = cci_data[[\"CCI.CCI\"]]\n",
    "\n",
    "# 填充NaN值\n",
    "df.fillna(method=\"bfill\", inplace=True)\n",
    "df.fillna(method=\"ffill\", inplace=True)\n",
    "df.fillna(0, inplace=True)\n",
    "\n",
    "# 显示添加技术指标后的数据预览\n",
    "print(\"添加技术指标后的特征数量:\", len(df.columns))\n",
    "print(\"新增技术指标列:\", df.columns[-13:].tolist())\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24637cf3",
   "metadata": {},
   "source": [
    "## LightGBM特征构造函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "34a00027",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LightGBM特征构造函数定义完成！\n"
     ]
    }
   ],
   "source": [
    "def create_lgb_features(stock_data, seq_length, features):\n",
    "    \"\"\"\n",
    "    为LightGBM创建时间窗口特征\n",
    "    Args:\n",
    "        stock_data: 单个股票的数据\n",
    "        seq_length: 序列长度\n",
    "        features: 特征列名列表\n",
    "    Returns:\n",
    "        X: 特征矩阵\n",
    "        y: 目标变量\n",
    "    \"\"\"\n",
    "    # 按日期排序\n",
    "    stock_data = stock_data.sort_values(\"Date\").reset_index(drop=True)\n",
    "\n",
    "    X_list = []\n",
    "    y_list = []\n",
    "\n",
    "    for i in range(seq_length, len(stock_data)):\n",
    "        # 获取过去seq_length天的特征\n",
    "        window_data = stock_data.iloc[i - seq_length : i][features].values\n",
    "\n",
    "        # 将时间窗口特征展平为一维向量\n",
    "        # 例如: 如果seq_length=10, features=20, 那么特征向量长度为200\n",
    "        X_list.append(window_data.flatten())\n",
    "\n",
    "        # 目标变量是当前时刻的涨跌幅\n",
    "        y_list.append(stock_data.iloc[i][\"PriceChangePercentage\"])\n",
    "\n",
    "    return np.array(X_list), np.array(y_list)\n",
    "\n",
    "\n",
    "def create_lgb_features_with_split(stock_data, seq_length, features, validation_days=5):\n",
    "    \"\"\"\n",
    "    为LightGBM创建时间窗口特征，并分割训练集和多个验证集\n",
    "\n",
    "    Args:\n",
    "        stock_data: 单个股票的数据\n",
    "        seq_length: 序列长度\n",
    "        features: 特征列名列表\n",
    "        validation_days: 验证集天数（创建多个子验证集）\n",
    "\n",
    "    Returns:\n",
    "        X_train, y_train: 训练集特征和标签\n",
    "        val_sets: 包含多个子验证集的列表，每个子验证集是一个元组(X_val, y_val, date)\n",
    "    \"\"\"\n",
    "    # 按日期排序\n",
    "    stock_data = stock_data.sort_values(\"Date\").reset_index(drop=True)\n",
    "\n",
    "    # 创建完整的特征矩阵\n",
    "    X_list = []\n",
    "    y_list = []\n",
    "    dates_list = []\n",
    "\n",
    "    for i in range(seq_length, len(stock_data)):\n",
    "        # 获取过去seq_length天的特征\n",
    "        window_data = stock_data.iloc[i - seq_length : i][features].values\n",
    "\n",
    "        # 将时间窗口特征展平为一维向量\n",
    "        X_list.append(window_data.flatten())\n",
    "\n",
    "        # 目标变量是当前时刻的涨跌幅\n",
    "        y_list.append(stock_data.iloc[i][\"PriceChangePercentage\"])\n",
    "\n",
    "        # 记录对应的日期\n",
    "        dates_list.append(stock_data.iloc[i][\"Date\"])\n",
    "\n",
    "    X_all = np.array(X_list)\n",
    "    y_all = np.array(y_list)\n",
    "    dates_all = np.array(dates_list)\n",
    "\n",
    "    # 如果数据量不足以创建验证集\n",
    "    if len(X_all) <= validation_days:\n",
    "        return X_all, y_all, []\n",
    "\n",
    "    # 创建多个子验证集\n",
    "    # 训练集：除去最后validation_days天的所有数据\n",
    "    train_size = len(X_all) - validation_days\n",
    "    X_train = X_all[:train_size]\n",
    "    y_train = y_all[:train_size]\n",
    "\n",
    "    # 创建验证集列表，每个验证集包含完整的seq_length历史特征\n",
    "    val_sets = []\n",
    "    for i in range(validation_days):\n",
    "        val_idx = train_size + i\n",
    "        if val_idx < len(X_all):\n",
    "            # 验证集实际上只有一个样本点，但这个样本点包含了seq_length天的特征数据\n",
    "            X_val = X_all[\n",
    "                val_idx : val_idx + 1\n",
    "            ]  # 保持二维数组形状 [1, seq_length*features]\n",
    "            y_val = y_all[val_idx : val_idx + 1]\n",
    "            date = dates_all[val_idx]\n",
    "            val_sets.append((X_val, y_val, date))\n",
    "\n",
    "    return X_train, y_train, val_sets\n",
    "\n",
    "\n",
    "def create_prediction_features(stock_data, seq_length, features, scaler):\n",
    "    \"\"\"\n",
    "    为预测阶段创建特征\n",
    "\n",
    "    Args:\n",
    "        stock_data: 单个股票的数据\n",
    "        seq_length: 序列长度\n",
    "        features: 特征列名列表\n",
    "        scaler: 标准化器\n",
    "    Returns:\n",
    "        X: 特征向量\n",
    "    \"\"\"\n",
    "    # 获取最后seq_length条记录\n",
    "    last_records = stock_data.iloc[-seq_length:].copy().reset_index(drop=True)\n",
    "\n",
    "    # 标准化特征（保留涨跌幅）\n",
    "    backup_PriceChangePercentage = last_records[\"PriceChangePercentage\"].copy()\n",
    "    last_records[features] = scaler.transform(last_records[features])\n",
    "    last_records[\"PriceChangePercentage\"] = backup_PriceChangePercentage\n",
    "\n",
    "    # 提取特征并展平\n",
    "    window_data = last_records[features].values\n",
    "    X = window_data.flatten().reshape(1, -1)\n",
    "\n",
    "    return X\n",
    "\n",
    "\n",
    "print(\"LightGBM特征构造函数定义完成！\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ec8e8c5",
   "metadata": {},
   "source": [
    "## 每个股票单独训练LightGBM模型"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ad4081d",
   "metadata": {},
   "source": [
    "### 加权平均MSE计算公式\n",
    "\n",
    "为了更准确地评估整体模型性能，我们将按照每个股票的数据点数量进行加权平均：\n",
    "\n",
    "$$\\text{总体MSE} = \\frac{\\sum_{i=1}^N n_i \\cdot \\text{MSE}_i}{\\sum_{i=1}^N n_i}$$\n",
    "\n",
    "其中：\n",
    "- $N$ 是股票总数\n",
    "- $n_i$ 是第 $i$ 支股票的数据点数量  \n",
    "- $\\text{MSE}_i$ 是第 $i$ 支股票LightGBM模型的MSE损失\n",
    "\n",
    "这种方法能够确保数据量大的股票在总体评估中有更大的权重，更准确地反映模型的整体性能。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "911d0d93",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "总共有 300 支股票需要训练\n",
      "\n",
      "==================================================\n",
      "训练股票 600000 (1/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2363, 320), 目标变量形状: (2363,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600000 训练完成\n",
      "  训练集MSE损失: 1.721536\n",
      "  训练集数据点数量: 2363\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600000.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600009 (2/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2384, 320), 目标变量形状: (2384,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600009 训练完成\n",
      "  训练集MSE损失: 2.999472\n",
      "  训练集数据点数量: 2384\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600009.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600010 (3/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2386, 320), 目标变量形状: (2386,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600010 训练完成\n",
      "  训练集MSE损失: 3.521701\n",
      "  训练集数据点数量: 2386\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600010.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600011 (4/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2395, 320), 目标变量形状: (2395,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600011 训练完成\n",
      "  训练集MSE损失: 3.635961\n",
      "  训练集数据点数量: 2395\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600011.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600015 (5/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2395, 320), 目标变量形状: (2395,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600015 训练完成\n",
      "  训练集MSE损失: 1.679780\n",
      "  训练集数据点数量: 2395\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600015.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600016 (6/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2395, 320), 目标变量形状: (2395,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600016 训练完成\n",
      "  训练集MSE损失: 1.646701\n",
      "  训练集数据点数量: 2395\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600016.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600018 (7/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2392, 320), 目标变量形状: (2392,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600018 训练完成\n",
      "  训练集MSE损失: 3.279137\n",
      "  训练集数据点数量: 2392\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600018.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600019 (8/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2303, 320), 目标变量形状: (2303,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600019 训练完成\n",
      "  训练集MSE损失: 7.031140\n",
      "  训练集数据点数量: 2303\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600019.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600023 (9/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2395, 320), 目标变量形状: (2395,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600023 训练完成\n",
      "  训练集MSE损失: 2.621684\n",
      "  训练集数据点数量: 2395\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600023.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600025 (10/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (1743, 320), 目标变量形状: (1743,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600025 训练完成\n",
      "  训练集MSE损失: 2.709137\n",
      "  训练集数据点数量: 1743\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600025.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600026 (11/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2297, 320), 目标变量形状: (2297,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600026 训练完成\n",
      "  训练集MSE损失: 6.124933\n",
      "  训练集数据点数量: 2297\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600026.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600027 (12/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2384, 320), 目标变量形状: (2384,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600027 训练完成\n",
      "  训练集MSE损失: 4.633677\n",
      "  训练集数据点数量: 2384\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600027.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600028 (13/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2395, 320), 目标变量形状: (2395,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600028 训练完成\n",
      "  训练集MSE损失: 4.105171\n",
      "  训练集数据点数量: 2395\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600028.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600029 (14/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2392, 320), 目标变量形状: (2392,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600029 训练完成\n",
      "  训练集MSE损失: 3.367878\n",
      "  训练集数据点数量: 2392\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600029.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600030 (15/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2378, 320), 目标变量形状: (2378,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600030 训练完成\n",
      "  训练集MSE损失: 3.175020\n",
      "  训练集数据点数量: 2378\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600030.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600031 (16/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2394, 320), 目标变量形状: (2394,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600031 训练完成\n",
      "  训练集MSE损失: 5.425220\n",
      "  训练集数据点数量: 2394\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600031.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600036 (17/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2395, 320), 目标变量形状: (2395,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600036 训练完成\n",
      "  训练集MSE损失: 5.560779\n",
      "  训练集数据点数量: 2395\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600036.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600039 (18/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2373, 320), 目标变量形状: (2373,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600039 训练完成\n",
      "  训练集MSE损失: 15.079825\n",
      "  训练集数据点数量: 2373\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600039.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600048 (19/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2388, 320), 目标变量形状: (2388,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600048 训练完成\n",
      "  训练集MSE损失: 7.101153\n",
      "  训练集数据点数量: 2388\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600048.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600050 (20/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2300, 320), 目标变量形状: (2300,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600050 训练完成\n",
      "  训练集MSE损失: 3.389703\n",
      "  训练集数据点数量: 2300\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600050.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600061 (21/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2355, 320), 目标变量形状: (2355,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600061 训练完成\n",
      "  训练集MSE损失: 3.856702\n",
      "  训练集数据点数量: 2355\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600061.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600066 (22/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2394, 320), 目标变量形状: (2394,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600066 训练完成\n",
      "  训练集MSE损失: 6.508377\n",
      "  训练集数据点数量: 2394\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600066.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600085 (23/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2395, 320), 目标变量形状: (2395,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600085 训练完成\n",
      "  训练集MSE损失: 3.599342\n",
      "  训练集数据点数量: 2395\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600085.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600089 (24/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2386, 320), 目标变量形状: (2386,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600089 训练完成\n",
      "  训练集MSE损失: 4.561115\n",
      "  训练集数据点数量: 2386\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600089.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600104 (25/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2375, 320), 目标变量形状: (2375,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600104 训练完成\n",
      "  训练集MSE损失: 4.362589\n",
      "  训练集数据点数量: 2375\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600104.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600111 (26/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2395, 320), 目标变量形状: (2395,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600111 训练完成\n",
      "  训练集MSE损失: 5.071665\n",
      "  训练集数据点数量: 2395\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600111.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600115 (27/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2387, 320), 目标变量形状: (2387,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600115 训练完成\n",
      "  训练集MSE损失: 3.016842\n",
      "  训练集数据点数量: 2387\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600115.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600150 (28/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2258, 320), 目标变量形状: (2258,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600150 训练完成\n",
      "  训练集MSE损失: 4.680999\n",
      "  训练集数据点数量: 2258\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600150.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600160 (29/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2374, 320), 目标变量形状: (2374,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600160 训练完成\n",
      "  训练集MSE损失: 5.970735\n",
      "  训练集数据点数量: 2374\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600160.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600161 (30/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2274, 320), 目标变量形状: (2274,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600161 训练完成\n",
      "  训练集MSE损失: 4.173990\n",
      "  训练集数据点数量: 2274\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600161.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600176 (31/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2373, 320), 目标变量形状: (2373,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600176 训练完成\n",
      "  训练集MSE损失: 6.352126\n",
      "  训练集数据点数量: 2373\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600176.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600183 (32/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2395, 320), 目标变量形状: (2395,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600183 训练完成\n",
      "  训练集MSE损失: 11.477915\n",
      "  训练集数据点数量: 2395\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600183.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600188 (33/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2394, 320), 目标变量形状: (2394,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600188 训练完成\n",
      "  训练集MSE损失: 22566.446871\n",
      "  训练集数据点数量: 2394\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600188.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600196 (34/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2393, 320), 目标变量形状: (2393,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600196 训练完成\n",
      "  训练集MSE损失: 4.463457\n",
      "  训练集数据点数量: 2393\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600196.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600219 (35/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2289, 320), 目标变量形状: (2289,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600219 训练完成\n",
      "  训练集MSE损失: 3.448692\n",
      "  训练集数据点数量: 2289\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600219.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600233 (36/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2298, 320), 目标变量形状: (2298,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600233 训练完成\n",
      "  训练集MSE损失: 5.207673\n",
      "  训练集数据点数量: 2298\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600233.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600276 (37/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2394, 320), 目标变量形状: (2394,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600276 训练完成\n",
      "  训练集MSE损失: 2.808606\n",
      "  训练集数据点数量: 2394\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600276.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600309 (38/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2262, 320), 目标变量形状: (2262,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600309 训练完成\n",
      "  训练集MSE损失: 1396.454751\n",
      "  训练集数据点数量: 2262\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600309.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600332 (39/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2346, 320), 目标变量形状: (2346,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600332 训练完成\n",
      "  训练集MSE损失: 3.453102\n",
      "  训练集数据点数量: 2346\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600332.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600346 (40/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2186, 320), 目标变量形状: (2186,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600346 训练完成\n",
      "  训练集MSE损失: 8.490207\n",
      "  训练集数据点数量: 2186\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600346.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600362 (41/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2387, 320), 目标变量形状: (2387,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600362 训练完成\n",
      "  训练集MSE损失: 4.499850\n",
      "  训练集数据点数量: 2387\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600362.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600372 (42/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2367, 320), 目标变量形状: (2367,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600372 训练完成\n",
      "  训练集MSE损失: 4.042333\n",
      "  训练集数据点数量: 2367\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600372.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600377 (43/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2395, 320), 目标变量形状: (2395,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600377 训练完成\n",
      "  训练集MSE损失: 3.295060\n",
      "  训练集数据点数量: 2395\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600377.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600406 (44/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2286, 320), 目标变量形状: (2286,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600406 训练完成\n",
      "  训练集MSE损失: 4.897020\n",
      "  训练集数据点数量: 2286\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600406.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600415 (45/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2388, 320), 目标变量形状: (2388,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600415 训练完成\n",
      "  训练集MSE损失: 4.510500\n",
      "  训练集数据点数量: 2388\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600415.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600426 (46/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2388, 320), 目标变量形状: (2388,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600426 训练完成\n",
      "  训练集MSE损失: 9.288748\n",
      "  训练集数据点数量: 2388\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600426.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600436 (47/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2389, 320), 目标变量形状: (2389,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600436 训练完成\n",
      "  训练集MSE损失: 4.067410\n",
      "  训练集数据点数量: 2389\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600436.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600438 (48/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2305, 320), 目标变量形状: (2305,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600438 训练完成\n",
      "  训练集MSE损失: 4705.724638\n",
      "  训练集数据点数量: 2305\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600438.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600460 (49/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2310, 320), 目标变量形状: (2310,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600460 训练完成\n",
      "  训练集MSE损失: 6.378582\n",
      "  训练集数据点数量: 2310\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600460.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600482 (50/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2272, 320), 目标变量形状: (2272,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600482 训练完成\n",
      "  训练集MSE损失: 2.992189\n",
      "  训练集数据点数量: 2272\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600482.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600489 (51/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2376, 320), 目标变量形状: (2376,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600489 训练完成\n",
      "  训练集MSE损失: 3.639629\n",
      "  训练集数据点数量: 2376\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600489.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600515 (52/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2113, 320), 目标变量形状: (2113,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600515 训练完成\n",
      "  训练集MSE损失: 4.102139\n",
      "  训练集数据点数量: 2113\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600515.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600519 (53/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2395, 320), 目标变量形状: (2395,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600519 训练完成\n",
      "  训练集MSE损失: 614323.034902\n",
      "  训练集数据点数量: 2395\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600519.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600547 (54/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2359, 320), 目标变量形状: (2359,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600547 训练完成\n",
      "  训练集MSE损失: 3.742146\n",
      "  训练集数据点数量: 2359\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600547.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600570 (55/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2388, 320), 目标变量形状: (2388,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600570 训练完成\n",
      "  训练集MSE损失: 5.897161\n",
      "  训练集数据点数量: 2388\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600570.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600584 (56/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2242, 320), 目标变量形状: (2242,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600584 训练完成\n",
      "  训练集MSE损失: 5.674899\n",
      "  训练集数据点数量: 2242\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600584.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600585 (57/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2395, 320), 目标变量形状: (2395,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600585 训练完成\n",
      "  训练集MSE损失: 71.712008\n",
      "  训练集数据点数量: 2395\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600585.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600588 (58/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2389, 320), 目标变量形状: (2389,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600588 训练完成\n",
      "  训练集MSE损失: 6.240661\n",
      "  训练集数据点数量: 2389\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600588.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600600 (59/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2395, 320), 目标变量形状: (2395,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600600 训练完成\n",
      "  训练集MSE损失: 4.058173\n",
      "  训练集数据点数量: 2395\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600600.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600660 (60/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2395, 320), 目标变量形状: (2395,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600660 训练完成\n",
      "  训练集MSE损失: 6.230486\n",
      "  训练集数据点数量: 2395\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600660.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600674 (61/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2395, 320), 目标变量形状: (2395,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600674 训练完成\n",
      "  训练集MSE损失: 2.521703\n",
      "  训练集数据点数量: 2395\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600674.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600690 (62/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2318, 320), 目标变量形状: (2318,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600690 训练完成\n",
      "  训练集MSE损失: 4.454041\n",
      "  训练集数据点数量: 2318\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600690.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600741 (63/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2392, 320), 目标变量形状: (2392,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600741 训练完成\n",
      "  训练集MSE损失: 6.169648\n",
      "  训练集数据点数量: 2392\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600741.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600745 (64/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2045, 320), 目标变量形状: (2045,)\n",
      "  创建的验证集数量: 5\n",
      "股票 600745 训练完成\n",
      "  训练集MSE损失: 6.344534\n",
      "  训练集数据点数量: 2045\n",
      "  验证集预测数量: 5\n",
      "  模型保存至: ./../../model\\lightgbm_model_600745.txt\n",
      "\n",
      "==================================================\n",
      "训练股票 600760 (65/300)\n",
      "==================================================\n",
      "  训练集特征矩阵形状: (2313, 320), 目标变量形状: (2313,)\n",
      "  创建的验证集数量: 5\n"
     ]
    }
   ],
   "source": [
    "# 获取特征列\n",
    "features = df.columns.difference([\"Date\", \"StockCode\"]).tolist()\n",
    "\n",
    "# 获取所有股票代码\n",
    "unique_stock_codes = df[\"StockCode\"].unique()\n",
    "print(f\"总共有 {len(unique_stock_codes)} 支股票需要训练\")\n",
    "\n",
    "# 存储每个股票的模型和结果\n",
    "stock_models = {}\n",
    "stock_scalers = {}\n",
    "stock_train_losses = {}  # 训练集损失\n",
    "stock_val_predictions = {}  # 验证集预测结果\n",
    "stock_val_true_values = {}  # 验证集真实值\n",
    "total_train_mse = 0.0\n",
    "total_weighted_train_mse = 0.0  # 加权训练集MSE总和\n",
    "total_train_points = 0  # 训练集总数据点数量\n",
    "\n",
    "\n",
    "# 训练每个股票的LightGBM模型\n",
    "for i, stock_code in enumerate(unique_stock_codes):\n",
    "    print(f\"\\n{'='*50}\")\n",
    "    print(f\"训练股票 {stock_code} ({i+1}/{len(unique_stock_codes)})\")\n",
    "    print(f\"{'='*50}\")\n",
    "\n",
    "    # 获取该股票的数据\n",
    "    stock_data = df[df[\"StockCode\"] == stock_code].copy()\n",
    "    stock_data = stock_data.sort_values(\"Date\").reset_index(drop=True)\n",
    "\n",
    "    if len(stock_data) < seq_len + validation_days + 1:\n",
    "        print(\n",
    "            f\"股票 {stock_code} 数据不足（需要至少{seq_len + validation_days + 1}天数据），跳过\"\n",
    "        )\n",
    "        continue\n",
    "\n",
    "    # 特征标准化（保留PriceChangePercentage原始值）\n",
    "    scaler = StandardScaler()\n",
    "    backup_PriceChangePercentage = stock_data[\"PriceChangePercentage\"].copy()\n",
    "    stock_data[features] = scaler.fit_transform(stock_data[features])\n",
    "    stock_data[\"PriceChangePercentage\"] = backup_PriceChangePercentage\n",
    "\n",
    "    # 保存标准化器\n",
    "    stock_scalers[stock_code] = scaler\n",
    "\n",
    "    # 创建LightGBM特征并分割训练集和验证集\n",
    "    X_train, y_train, val_sets = create_lgb_features_with_split(\n",
    "        stock_data, seq_len, features, validation_days\n",
    "    )\n",
    "\n",
    "    if len(X_train) == 0:\n",
    "        print(f\"股票 {stock_code} 训练集数据为空，跳过\")\n",
    "        continue\n",
    "\n",
    "    print(f\"  训练集特征矩阵形状: {X_train.shape}, 目标变量形状: {y_train.shape}\")\n",
    "    print(f\"  创建的验证集数量: {len(val_sets)}\")\n",
    "\n",
    "    # 创建LightGBM数据集\n",
    "    train_data = lgb.Dataset(X_train, label=y_train)\n",
    "\n",
    "    # 训练LightGBM模型\n",
    "    model = lgb.train(\n",
    "        lgb_params,\n",
    "        train_data,\n",
    "        valid_sets=[train_data],\n",
    "        valid_names=[\"train\"],\n",
    "        callbacks=[lgb.early_stopping(stopping_rounds=10), lgb.log_evaluation(0)],\n",
    "    )\n",
    "\n",
    "    # 计算训练集MSE\n",
    "    y_train_pred = model.predict(X_train)\n",
    "    train_mse = mean_squared_error(y_train, y_train_pred)\n",
    "\n",
    "    # 记录该股票的训练损失和数据点数量\n",
    "    stock_train_losses[stock_code] = train_mse\n",
    "    total_train_mse += train_mse\n",
    "    total_weighted_train_mse += train_mse * len(X_train)\n",
    "    total_train_points += len(X_train)\n",
    "\n",
    "    # 在每个验证集上进行预测\n",
    "    stock_val_predictions[stock_code] = []\n",
    "    stock_val_true_values[stock_code] = []\n",
    "\n",
    "    for X_val, y_val, date in val_sets:\n",
    "        # 在验证集上预测\n",
    "        y_val_pred = model.predict(X_val)\n",
    "        stock_val_predictions[stock_code].append((date, y_val_pred[0]))\n",
    "        stock_val_true_values[stock_code].append((date, y_val[0]))\n",
    "\n",
    "    # 保存模型\n",
    "    model_path = os.path.join(MODEL_DIR, f\"lightgbm_model_{stock_code}.txt\")\n",
    "    model.save_model(model_path)\n",
    "    stock_models[stock_code] = model_path\n",
    "\n",
    "    print(f\"股票 {stock_code} 训练完成\")\n",
    "    print(f\"  训练集MSE损失: {train_mse:.6f}\")\n",
    "    print(f\"  训练集数据点数量: {len(X_train)}\")\n",
    "    print(f\"  验证集预测数量: {len(stock_val_predictions[stock_code])}\")\n",
    "    print(f\"  模型保存至: {model_path}\")\n",
    "\n",
    "print(f\"\\n{'='*60}\")\n",
    "print(f\"所有股票训练完成！\")\n",
    "print(f\"成功训练的股票数量: {len(stock_models)}\")\n",
    "\n",
    "# 训练集统计\n",
    "print(f\"\\n训练集统计:\")\n",
    "print(f\"- 所有股票训练集MSE总和: {total_train_mse:.6f}\")\n",
    "print(f\"- 训练集简单平均MSE: {total_train_mse/len(stock_models):.6f}\")\n",
    "print(f\"- 训练集总数据点数量: {total_train_points}\")\n",
    "weighted_avg_train_mse = (\n",
    "    total_weighted_train_mse / total_train_points if total_train_points > 0 else 0\n",
    ")\n",
    "print(f\"- 训练集按数据点数量加权平均MSE: {weighted_avg_train_mse:.6f}\")\n",
    "\n",
    "print(f\"{'='*60}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb61a84b",
   "metadata": {},
   "source": [
    "## 预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d539009d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "开始在测试集上进行预测...\n",
      "测试集数据形状: (637229, 12)\n",
      "测试集股票数量: 300\n",
      "完成了 300 支股票的预测\n",
      "\n",
      "预测涨幅最大的10支股票: [np.int64(600438), np.int64(408), np.int64(600188), np.int64(600809), np.int64(651), np.int64(600585), np.int64(333), np.int64(601919), np.int64(895), np.int64(2714)]\n",
      "预测涨幅最小的10支股票: [np.int64(600845), np.int64(601328), np.int64(601998), np.int64(2252), np.int64(601699), np.int64(600039), np.int64(601088), np.int64(858), np.int64(568), np.int64(600519)]\n",
      "预测结果已保存到: ./../../output\\lightgbm_prediction_result.csv\n"
     ]
    }
   ],
   "source": [
    "# 加载测试数据\n",
    "def make_predictions_on_test():\n",
    "    print(\"开始在测试集上进行预测...\")\n",
    "\n",
    "    # 尝试加载测试数据\n",
    "    try:\n",
    "        test_df = pd.read_csv(os.path.join(DATA_DIR, \"test.csv\"))\n",
    "        # 应用列名映射\n",
    "        test_df.rename(columns=column_mapping, inplace=True)\n",
    "        print(f\"测试集数据形状: {test_df.shape}\")\n",
    "        print(f\"测试集股票数量: {test_df['StockCode'].nunique()}\")\n",
    "    except Exception as e:\n",
    "        print(f\"加载测试集数据失败: {e}\")\n",
    "        return None\n",
    "\n",
    "    # 存储每个股票的预测结果\n",
    "    stock_predictions = {}\n",
    "\n",
    "    # 对测试集中的每个股票进行预测\n",
    "    for stock_code in test_df[\"StockCode\"].unique():\n",
    "        # 检查是否有该股票的模型\n",
    "        if stock_code not in stock_models:\n",
    "            print(f\"股票 {stock_code} 没有训练模型，跳过预测\")\n",
    "            continue\n",
    "\n",
    "        # 获取该股票的测试数据\n",
    "        stock_test_data = test_df[test_df[\"StockCode\"] == stock_code].copy()\n",
    "\n",
    "        # 检查测试数据是否足够\n",
    "        if len(stock_test_data) < seq_len:\n",
    "            print(f\"股票 {stock_code} 测试数据不足 {seq_len} 条，跳过预测\")\n",
    "            continue\n",
    "\n",
    "        # 加载模型和标准化器\n",
    "        model_path = stock_models[stock_code]\n",
    "        scaler = stock_scalers[stock_code]\n",
    "        model = lgb.Booster(model_file=model_path)\n",
    "\n",
    "        # 添加技术指标特征\n",
    "        # 计算移动平均线\n",
    "        stock_test_data[\"MA.MA1\"] = stock_test_data[\"Close\"].rolling(window=5).mean()\n",
    "        stock_test_data[\"MA.MA2\"] = stock_test_data[\"Close\"].rolling(window=10).mean()\n",
    "        stock_test_data[\"MA.MA3\"] = stock_test_data[\"Close\"].rolling(window=20).mean()\n",
    "        stock_test_data[\"MA.MA4\"] = stock_test_data[\"Close\"].rolling(window=30).mean()\n",
    "        stock_test_data[\"MA.MA5\"] = stock_test_data[\"Close\"].rolling(window=60).mean()\n",
    "        stock_test_data[\"MA.MA6\"] = stock_test_data[\"Close\"].rolling(window=120).mean()\n",
    "\n",
    "        # 计算KDJ指标\n",
    "        kdj_data = calculate_kdj(stock_test_data)\n",
    "        stock_test_data[[\"KDJ.K\", \"KDJ.D\", \"KDJ.J\"]] = kdj_data[\n",
    "            [\"KDJ.K\", \"KDJ.D\", \"KDJ.J\"]\n",
    "        ]\n",
    "\n",
    "        # 计算MACD指标\n",
    "        macd_data = calculate_macd(stock_test_data)\n",
    "        stock_test_data[[\"MACD.DIFF\", \"MACD.DEA\", \"MACD.MACD\"]] = macd_data[\n",
    "            [\"MACD.DIFF\", \"MACD.DEA\", \"MACD.MACD\"]\n",
    "        ]\n",
    "\n",
    "        # 计算CCI指标\n",
    "        cci_data = calculate_cci(stock_test_data)\n",
    "        stock_test_data[\"CCI.CCI\"] = cci_data[\"CCI.CCI\"]\n",
    "\n",
    "        # 填充NaN值\n",
    "        stock_test_data.fillna(method=\"bfill\", inplace=True)\n",
    "        stock_test_data.fillna(method=\"ffill\", inplace=True)\n",
    "        stock_test_data.fillna(0, inplace=True)\n",
    "\n",
    "        # 标准化特征\n",
    "        stock_test_data[features] = scaler.transform(stock_test_data[features])\n",
    "\n",
    "        # 创建预测特征\n",
    "        X = create_prediction_features(stock_test_data, seq_len, features, scaler)\n",
    "\n",
    "        # 进行预测（预测涨跌幅）\n",
    "        pred_change_percentage = model.predict(X)[0]\n",
    "\n",
    "        # 存储预测结果\n",
    "        stock_predictions[stock_code] = pred_change_percentage\n",
    "\n",
    "    print(f\"完成了 {len(stock_predictions)} 支股票的预测\")\n",
    "\n",
    "    # 按预测涨跌幅排序\n",
    "    sorted_predictions = sorted(\n",
    "        stock_predictions.items(), key=lambda x: x[1], reverse=True\n",
    "    )\n",
    "\n",
    "    # 获取预测涨幅最大和最小的10支股票\n",
    "    top_10_stocks = [x[0] for x in sorted_predictions[:10]]\n",
    "    bottom_10_stocks = [x[0] for x in sorted_predictions[-10:]]\n",
    "\n",
    "    print(f\"\\n预测涨幅最大的10支股票: {top_10_stocks}\")\n",
    "    print(f\"预测涨幅最小的10支股票: {bottom_10_stocks}\")\n",
    "\n",
    "    # 创建提交格式的DataFrame\n",
    "    result_df = pd.DataFrame(\n",
    "        {\"涨幅最大股票代码\": top_10_stocks, \"涨幅最小股票代码\": bottom_10_stocks}\n",
    "    )\n",
    "\n",
    "    # 保存预测结果\n",
    "    result_path = os.path.join(OUTPUT_DIR, \"lightgbm_prediction_result.csv\")\n",
    "    result_df.to_csv(result_path, index=False)\n",
    "    print(f\"预测结果已保存到: {result_path}\")\n",
    "\n",
    "    return result_df\n",
    "\n",
    "\n",
    "# 执行测试集预测\n",
    "test_predictions = make_predictions_on_test()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "daa9c7c3",
   "metadata": {},
   "source": [
    "## 验证集评分计算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e62f00c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "开始滚动验证集评估...\n",
      "滚动验证集日期数量: 5\n",
      "\n",
      "评估日期 2025-04-14 的预测结果:\n",
      "  预测涨幅最大的10支股票: [np.int64(601699), np.int64(600809), np.int64(300979), np.int64(300308), np.int64(601899), np.int64(876), np.int64(603296), np.int64(600026), np.int64(300347), np.int64(688223)]\n",
      "  预测涨幅最小的10支股票: [np.int64(600674), np.int64(100), np.int64(2049), np.int64(2180), np.int64(688009), np.int64(688472), np.int64(301269), np.int64(600875), np.int64(300122), np.int64(600519)]\n",
      "  真实涨幅最大的10支股票: [np.int64(601127), np.int64(601225), np.int64(603993), np.int64(601898), np.int64(600875), np.int64(600547), np.int64(975), np.int64(300308), np.int64(617), np.int64(688256)]\n",
      "  真实涨幅最小的10支股票: [np.int64(600031), np.int64(600809), np.int64(603833), np.int64(2648), np.int64(651), np.int64(300782), np.int64(601100), np.int64(603369), np.int64(2180), np.int64(2028)]\n",
      "  日期 2025-04-14 的评分结果:\n",
      "  - 涨幅最大股票F1得分: 0.1000\n",
      "  - 涨幅最小股票F1得分: 0.1000\n",
      "  - 涨幅最大股票排序相关性: -0.2901\n",
      "  - 涨幅最小股票排序相关性: -0.4062\n",
      "  - 最终得分: -0.1689\n",
      "\n",
      "评估日期 2025-04-15 的预测结果:\n",
      "  预测涨幅最大的10支股票: [np.int64(600188), np.int64(601088), np.int64(688256), np.int64(601699), np.int64(688223), np.int64(688981), np.int64(600028), np.int64(601288), np.int64(876), np.int64(300979)]\n",
      "  预测涨幅最小的10支股票: [np.int64(601689), np.int64(603501), np.int64(688169), np.int64(300433), np.int64(601888), np.int64(688506), np.int64(600519), np.int64(2049), np.int64(600415), np.int64(301269)]\n",
      "  真实涨幅最大的10支股票: [np.int64(688506), np.int64(2001), np.int64(300450), np.int64(999), np.int64(600000), np.int64(603659), np.int64(601838), np.int64(605499), np.int64(601319), np.int64(2555)]\n",
      "  真实涨幅最小的10支股票: [np.int64(688256), np.int64(617), np.int64(300832), np.int64(2180), np.int64(601888), np.int64(688036), np.int64(601100), np.int64(2475), np.int64(301269), np.int64(600309)]\n",
      "  日期 2025-04-15 的评分结果:\n",
      "  - 涨幅最大股票F1得分: 0.0000\n",
      "  - 涨幅最小股票F1得分: 0.2000\n",
      "  - 涨幅最大股票排序相关性: 0.0000\n",
      "  - 涨幅最小股票排序相关性: -0.2249\n",
      "  - 最终得分: -0.0275\n",
      "\n",
      "评估日期 2025-04-16 的预测结果:\n",
      "  预测涨幅最大的10支股票: [np.int64(601699), np.int64(688256), np.int64(688223), np.int64(300059), np.int64(300274), np.int64(2180), np.int64(600482), np.int64(601607), np.int64(600104), np.int64(300033)]\n",
      "  预测涨幅最小的10支股票: [np.int64(600519), np.int64(688126), np.int64(2179), np.int64(3816), np.int64(688041), np.int64(601888), np.int64(600436), np.int64(2050), np.int64(600588), np.int64(600188)]\n",
      "  真实涨幅最大的10支股票: [np.int64(975), np.int64(601816), np.int64(688256), np.int64(603392), np.int64(601111), np.int64(603833), np.int64(300661), np.int64(2371), np.int64(601021), np.int64(603195)]\n",
      "  真实涨幅最小的10支股票: [np.int64(601689), np.int64(600161), np.int64(2475), np.int64(2463), np.int64(2938), np.int64(600183), np.int64(2648), np.int64(300502), np.int64(300979), np.int64(603296)]\n",
      "  日期 2025-04-16 的评分结果:\n",
      "  - 涨幅最大股票F1得分: 0.1000\n",
      "  - 涨幅最小股票F1得分: 0.0000\n",
      "  - 涨幅最大股票排序相关性: 0.2901\n",
      "  - 涨幅最小股票排序相关性: 0.0000\n",
      "  - 最终得分: 0.1070\n",
      "\n",
      "评估日期 2025-04-17 的预测结果:\n",
      "  预测涨幅最大的10支股票: [np.int64(688256), np.int64(600989), np.int64(688223), np.int64(601699), np.int64(617), np.int64(600460), np.int64(300308), np.int64(2230), np.int64(600938), np.int64(605117)]\n",
      "  预测涨幅最小的10支股票: [np.int64(688111), np.int64(600690), np.int64(601888), np.int64(2050), np.int64(977), np.int64(688303), np.int64(600547), np.int64(600519), np.int64(876), np.int64(600489)]\n",
      "  真实涨幅最大的10支股票: [np.int64(300661), np.int64(688256), np.int64(2271), np.int64(600048), np.int64(300760), np.int64(786), np.int64(603833), np.int64(605499), np.int64(600809), np.int64(2)]\n",
      "  真实涨幅最小的10支股票: [np.int64(600377), np.int64(601888), np.int64(688126), np.int64(603392), np.int64(617), np.int64(2648), np.int64(688303), np.int64(600066), np.int64(975), np.int64(601127)]\n",
      "  日期 2025-04-17 的评分结果:\n",
      "  - 涨幅最大股票F1得分: 0.1000\n",
      "  - 涨幅最小股票F1得分: 0.2000\n",
      "  - 涨幅最大股票排序相关性: 0.4062\n",
      "  - 涨幅最小股票排序相关性: 0.2162\n",
      "  - 最终得分: 0.2467\n",
      "\n",
      "评估日期 2025-04-18 的预测结果:\n",
      "  预测涨幅最大的10支股票: [np.int64(601225), np.int64(688256), np.int64(603019), np.int64(601699), np.int64(600372), np.int64(300442), np.int64(2463), np.int64(963), np.int64(605117), np.int64(300394)]\n",
      "  预测涨幅最小的10支股票: [np.int64(600438), np.int64(688111), np.int64(688126), np.int64(601698), np.int64(977), np.int64(600489), np.int64(333), np.int64(617), np.int64(2648), np.int64(600519)]\n",
      "  真实涨幅最大的10支股票: [np.int64(2463), np.int64(963), np.int64(300433), np.int64(63), np.int64(2555), np.int64(300502), np.int64(603799), np.int64(807), np.int64(425), np.int64(2241)]\n",
      "  真实涨幅最小的10支股票: [np.int64(1289), np.int64(600588), np.int64(300896), np.int64(688506), np.int64(688041), np.int64(603288), np.int64(603392), np.int64(300661), np.int64(975), np.int64(605499)]\n",
      "  日期 2025-04-18 的评分结果:\n",
      "  - 涨幅最大股票F1得分: 0.2000\n",
      "  - 涨幅最小股票F1得分: 0.0000\n",
      "  - 涨幅最大股票排序相关性: 0.7006\n",
      "  - 涨幅最小股票排序相关性: 0.0000\n",
      "  - 最终得分: 0.2502\n",
      "\n",
      "所有验证集日期的平均评分结果:\n",
      "- 涨幅最大股票平均F1得分: 0.1000\n",
      "- 涨幅最小股票平均F1得分: 0.1000\n",
      "- 涨幅最大股票平均排序相关性: 0.2214\n",
      "- 涨幅最小股票平均排序相关性: -0.0830\n",
      "- 平均最终得分: 0.0815\n",
      "\n",
      "验证集评分结果已保存到: ./../../output\\lightgbm_validation_scores.csv\n"
     ]
    }
   ],
   "source": [
    "# 验证集评分计算\n",
    "from scipy.stats import spearmanr\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "def calculate_final_score_from_predictions(pred_data, true_data):\n",
    "    \"\"\"\n",
    "    根据预测数据和真实数据计算最终得分\n",
    "\n",
    "    Parameters:\n",
    "    - pred_data: DataFrame with columns ['涨幅最大股票代码', '涨幅最小股票代码']\n",
    "    - true_data: DataFrame with columns ['涨幅最大股票代码', '涨幅最小股票代码']\n",
    "\n",
    "    Returns:\n",
    "    - float: Final score\n",
    "    \"\"\"\n",
    "    # Extract top 10 and bottom 10 stock codes\n",
    "    pred_top_10 = pred_data[\"涨幅最大股票代码\"].tolist()\n",
    "    pred_bottom_10 = pred_data[\"涨幅最小股票代码\"].tolist()\n",
    "    true_top_10 = true_data[\"涨幅最大股票代码\"].tolist()\n",
    "    true_bottom_10 = true_data[\"涨幅最小股票代码\"].tolist()\n",
    "\n",
    "    # Calculate F1 scores for top 10\n",
    "    true_pos_top = len(set(true_top_10) & set(pred_top_10))\n",
    "    precision_up = true_pos_top / 10\n",
    "    recall_up = true_pos_top / 10\n",
    "    f1_up = (\n",
    "        (2 * precision_up * recall_up) / (precision_up + recall_up)\n",
    "        if (precision_up + recall_up) > 0\n",
    "        else 0\n",
    "    )\n",
    "\n",
    "    # Calculate F1 scores for bottom 10\n",
    "    true_pos_bottom = len(set(true_bottom_10) & set(pred_bottom_10))\n",
    "    precision_down = true_pos_bottom / 10\n",
    "    recall_down = true_pos_bottom / 10\n",
    "    f1_down = (\n",
    "        (2 * precision_down * recall_down) / (precision_down + recall_down)\n",
    "        if (precision_down + recall_down) > 0\n",
    "        else 0\n",
    "    )\n",
    "\n",
    "    # Compute predicted ranks based on order in predicted lists\n",
    "    true_top_ranks = np.arange(10)  # 0 to 9 for true top 10\n",
    "    pred_top_ranks = np.array(\n",
    "        [\n",
    "            pred_top_10.index(stock) if stock in pred_top_10 else 10\n",
    "            for stock in true_top_10\n",
    "        ]\n",
    "    )\n",
    "    true_bottom_ranks = np.arange(10)  # 0 to 9 for true bottom 10\n",
    "    pred_bottom_ranks = np.array(\n",
    "        [\n",
    "            pred_bottom_10.index(stock) if stock in pred_bottom_10 else 10\n",
    "            for stock in true_bottom_10\n",
    "        ]\n",
    "    )\n",
    "\n",
    "    pred_top_ranks = np.where(pred_top_ranks == 10, 11, pred_top_ranks)\n",
    "    pred_bottom_ranks = np.where(pred_bottom_ranks == 10, 11, pred_bottom_ranks)\n",
    "\n",
    "    try:\n",
    "        rank_corr_up, _ = spearmanr(true_top_ranks, pred_top_ranks)\n",
    "        if np.isnan(rank_corr_up):\n",
    "            rank_corr_up = 0.0\n",
    "    except Exception:\n",
    "        rank_corr_up = 0.0\n",
    "\n",
    "    try:\n",
    "        rank_corr_down, _ = spearmanr(true_bottom_ranks, pred_bottom_ranks)\n",
    "        if np.isnan(rank_corr_down):\n",
    "            rank_corr_down = 0.0\n",
    "    except Exception:\n",
    "        rank_corr_down = 0.0\n",
    "\n",
    "    final_score = (\n",
    "        0.2 * f1_up + 0.2 * f1_down + 0.3 * rank_corr_up + 0.3 * rank_corr_down\n",
    "    )\n",
    "\n",
    "    return final_score, f1_up, f1_down, rank_corr_up, rank_corr_down\n",
    "\n",
    "\n",
    "def evaluate_validation_predictions():\n",
    "    \"\"\"\n",
    "    使用滚动验证集评估模型预测性能\n",
    "    \"\"\"\n",
    "    print(\"开始滚动验证集评估...\")\n",
    "\n",
    "    # 检查是否有验证集数据\n",
    "    if not stock_val_predictions:\n",
    "        print(\"没有验证集数据，无法进行验证集评估\")\n",
    "        return\n",
    "\n",
    "    # 按日期组织验证集预测结果\n",
    "    date_predictions = {}\n",
    "    date_true_values = {}\n",
    "\n",
    "    # 整理每个日期的预测和真实值\n",
    "    for stock_code, predictions in stock_val_predictions.items():\n",
    "        for date, pred_value in predictions:\n",
    "            if date not in date_predictions:\n",
    "                date_predictions[date] = []\n",
    "                date_true_values[date] = []\n",
    "            date_predictions[date].append((stock_code, pred_value))\n",
    "\n",
    "    # 整理每个日期的真实值\n",
    "    for stock_code, true_values in stock_val_true_values.items():\n",
    "        for date, true_value in true_values:\n",
    "            if date in date_predictions:  # 确保有对应的预测\n",
    "                date_true_values[date].append((stock_code, true_value))\n",
    "\n",
    "    print(f\"滚动验证集日期数量: {len(date_predictions)}\")\n",
    "\n",
    "    # 存储每个验证日期的评估结果\n",
    "    date_scores = {}\n",
    "    all_f1_up = []\n",
    "    all_f1_down = []\n",
    "    all_rank_corr_up = []\n",
    "    all_rank_corr_down = []\n",
    "    all_final_scores = []\n",
    "\n",
    "    # 按日期进行评估\n",
    "    for date in sorted(date_predictions.keys()):\n",
    "        print(f\"\\n评估日期 {date} 的预测结果:\")\n",
    "\n",
    "        # 获取该日期的预测和真实值\n",
    "        pred_list = date_predictions[date]\n",
    "        true_list = date_true_values[date]\n",
    "\n",
    "        # 确保数据足够\n",
    "        if len(pred_list) < 10 or len(true_list) < 10:\n",
    "            print(f\"  日期 {date} 的股票数量不足10支，跳过评估\")\n",
    "            continue\n",
    "\n",
    "        # 按预测涨跌幅排序\n",
    "        pred_sorted = sorted(pred_list, key=lambda x: x[1], reverse=True)\n",
    "        true_sorted = sorted(true_list, key=lambda x: x[1], reverse=True)\n",
    "\n",
    "        # 获取前10和后10\n",
    "        pred_top_10 = [x[0] for x in pred_sorted[:10]]\n",
    "        pred_bottom_10 = [x[0] for x in pred_sorted[-10:]]\n",
    "\n",
    "        true_top_10 = [x[0] for x in true_sorted[:10]]\n",
    "        true_bottom_10 = [x[0] for x in true_sorted[-10:]]\n",
    "\n",
    "        print(f\"  预测涨幅最大的10支股票: {pred_top_10}\")\n",
    "        print(f\"  预测涨幅最小的10支股票: {pred_bottom_10}\")\n",
    "        print(f\"  真实涨幅最大的10支股票: {true_top_10}\")\n",
    "        print(f\"  真实涨幅最小的10支股票: {true_bottom_10}\")\n",
    "\n",
    "        # 创建DataFrame格式\n",
    "        pred_data = pd.DataFrame(\n",
    "            {\"涨幅最大股票代码\": pred_top_10, \"涨幅最小股票代码\": pred_bottom_10}\n",
    "        )\n",
    "\n",
    "        true_data = pd.DataFrame(\n",
    "            {\"涨幅最大股票代码\": true_top_10, \"涨幅最小股票代码\": true_bottom_10}\n",
    "        )\n",
    "\n",
    "        # 计算该日期的评分\n",
    "        final_score, f1_up, f1_down, rank_corr_up, rank_corr_down = (\n",
    "            calculate_final_score_from_predictions(pred_data, true_data)\n",
    "        )\n",
    "\n",
    "        # 记录评分\n",
    "        date_scores[date] = final_score\n",
    "        all_f1_up.append(f1_up)\n",
    "        all_f1_down.append(f1_down)\n",
    "        all_rank_corr_up.append(rank_corr_up)\n",
    "        all_rank_corr_down.append(rank_corr_down)\n",
    "        all_final_scores.append(final_score)\n",
    "\n",
    "        print(f\"  日期 {date} 的评分结果:\")\n",
    "        print(f\"  - 涨幅最大股票F1得分: {f1_up:.4f}\")\n",
    "        print(f\"  - 涨幅最小股票F1得分: {f1_down:.4f}\")\n",
    "        print(f\"  - 涨幅最大股票排序相关性: {rank_corr_up:.4f}\")\n",
    "        print(f\"  - 涨幅最小股票排序相关性: {rank_corr_down:.4f}\")\n",
    "        print(f\"  - 最终得分: {final_score:.4f}\")\n",
    "\n",
    "    # 计算所有验证集日期的平均评分\n",
    "    if all_final_scores:\n",
    "        avg_final_score = np.mean(all_final_scores)\n",
    "        avg_f1_up = np.mean(all_f1_up)\n",
    "        avg_f1_down = np.mean(all_f1_down)\n",
    "        avg_rank_corr_up = np.mean(all_rank_corr_up)\n",
    "        avg_rank_corr_down = np.mean(all_rank_corr_down)\n",
    "\n",
    "        print(f\"\\n所有验证集日期的平均评分结果:\")\n",
    "        print(f\"- 涨幅最大股票平均F1得分: {avg_f1_up:.4f}\")\n",
    "        print(f\"- 涨幅最小股票平均F1得分: {avg_f1_down:.4f}\")\n",
    "        print(f\"- 涨幅最大股票平均排序相关性: {avg_rank_corr_up:.4f}\")\n",
    "        print(f\"- 涨幅最小股票平均排序相关性: {avg_rank_corr_down:.4f}\")\n",
    "        print(f\"- 平均最终得分: {avg_final_score:.4f}\")\n",
    "\n",
    "        # 保存验证集评分结果\n",
    "        validation_results = pd.DataFrame(\n",
    "            {\n",
    "                \"日期\": list(date_scores.keys()),\n",
    "                \"最终得分\": list(date_scores.values()),\n",
    "            }\n",
    "        )\n",
    "\n",
    "        val_results_path = os.path.join(OUTPUT_DIR, \"lightgbm_validation_scores.csv\")\n",
    "        validation_results.to_csv(val_results_path, index=False)\n",
    "        print(f\"\\n验证集评分结果已保存到: {val_results_path}\")\n",
    "\n",
    "        return avg_final_score\n",
    "    else:\n",
    "        print(\"没有完成任何验证集日期的评估\")\n",
    "        return None\n",
    "\n",
    "\n",
    "# 执行滚动验证集评估\n",
    "validation_score = evaluate_validation_predictions()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b50b8450",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 设置随机种子以便结果可复现\n",
    "np.random.seed(42)\n",
    "\n",
    "# 随机选择股票数量\n",
    "random_stock_num = 5\n",
    "\n",
    "# 从所有拥有验证数据的股票中随机选择\n",
    "stocks_with_predictions = list(stock_val_true_values.keys())\n",
    "if len(stocks_with_predictions) > random_stock_num:\n",
    "    selected_stocks = random.sample(stocks_with_predictions, random_stock_num)\n",
    "else:\n",
    "    selected_stocks = stocks_with_predictions\n",
    "\n",
    "# 创建一个大图\n",
    "plt.figure(figsize=(14, 8))\n",
    "\n",
    "# 定义不同的颜色和标记样式\n",
    "colors = [\"blue\", \"red\", \"green\", \"purple\", \"orange\"]\n",
    "markers_pred = [\"o\", \"s\", \"^\", \"D\", \"p\"]\n",
    "markers_true = [\"*\", \"x\", \"+\", \"v\", \"1\"]\n",
    "\n",
    "# 在同一张图上绘制所有选定的股票\n",
    "for i, stock_code in enumerate(selected_stocks):\n",
    "    # 获取该股票的验证预测和真实值\n",
    "    pred_data = stock_val_predictions[stock_code]\n",
    "    true_data = stock_val_true_values[stock_code]\n",
    "\n",
    "    # 提取日期、预测值和真实值\n",
    "    dates = [item[0] for item in pred_data]\n",
    "    pred_values = [item[1] for item in pred_data]\n",
    "    true_values = [item[1] for item in true_data]\n",
    "\n",
    "    # 绘制折线图，使用不同颜色和标记区分不同股票\n",
    "    plt.plot(\n",
    "        range(len(dates)),\n",
    "        pred_values,\n",
    "        color=colors[i],\n",
    "        marker=markers_pred[i],\n",
    "        linestyle=\"-\",\n",
    "        label=f\"股票{stock_code}预测值\",\n",
    "    )\n",
    "    plt.plot(\n",
    "        range(len(dates)),\n",
    "        true_values,\n",
    "        color=colors[i],\n",
    "        marker=markers_true[i],\n",
    "        linestyle=\"--\",\n",
    "        label=f\"股票{stock_code}真实值\",\n",
    "    )\n",
    "\n",
    "# 设置图表\n",
    "plt.title(\"多支股票预测值与真实值对比\", fontsize=14)\n",
    "plt.ylabel(\"涨跌幅 (%)\", fontsize=12)\n",
    "plt.xlabel(\"日期\", fontsize=12)\n",
    "plt.xticks(range(len(dates)), [date[-5:] for date in dates], rotation=45)\n",
    "plt.grid(True, linestyle=\"--\", alpha=0.7)\n",
    "plt.axhline(y=0, color=\"gray\", linestyle=\"-\", alpha=0.3)\n",
    "plt.legend(loc=\"upper center\", bbox_to_anchor=(0.5, -0.15), ncol=3)\n",
    "\n",
    "# 调整布局\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c24af8e7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
